Aiming to address the challenges of the current power system fault risk warning methods in accurately mapping the meteorological elements and faults with complex coupling and its incapability in realizing multi-dimensional meteorological feature extraction under multiple types of extreme weather as well as the problem of low fault warning accuracy,in this paper we propose a combination of supervised and unsupervised learning as an accurate warning method for extreme weather-induced faults.First,the meteorological elements and fault correlations at the moment of failure in extreme weather scenarios are analyzed,and the hierarchical analysis method is utilized to assign weights to the meteorological elements,so as to realize the risk-adaptive assignments between the meteorological elements and the faults.Subsequently,a supervised network based on the random forest algorithm is embedded after the decoder of the unsupervised learning network,aiming to enhance the correlation between the risk weight features and power faults.Finally,the support vector mechanism is employed to construct an early warning model.The risk weight features,equipment data and environmental data are utilized as inputs to realize the fault early warning of the power system.Experimental analysis demonstrates that the fault warning accuracy reaches 87.13%,with the warning system capable of providing alerts 15.19 minutes prior to the occurrence of incidents.
Short-term load forecasting is crucial for optimizing energy supply and demand balance,which improves the efficiency of power system operation.In regions where historical load data are scarce,traditional machine learning prediction methods face great challenges.To address the short-term load forecasting issue in the context of sparse data,we propose a hybrid prediction model based on temporal convolutional network(TCN)and transfer learning.Firstly,a source domain selection method combining Mahalanobis distance and maximum mean discrepancy is introduced to measure the transferability of the source domain.This method aims to identify a source domain that is highly similar to the target domain in terms of data distribution and feature differences.Secondly,the snow ablation optimizer(SAO)is utilized to optimize the hyperparameters of the TCN model,thereby establishing an SAO-TCN transfer model to enhance overall prediction performance.Finally,the proposed hybrid model is evaluated and validated using real-world datasets.Experimental results demonstrate that in comparison to traditional machine learning methods,the hybrid model is capable of expanding the range of source domain selection and enhancing its accuracy as well,and it reduces the root mean square error by at least 15%.
A dual-layer optimization configuration model for hydrogen-containing comprehensive integrated parks under a steppedwise carbon trading scheme has been is proposed to better enhance optimize the low-carbon and economic operation within the comprehensive park.Firstly,heat storage and hydrogen storage systems are set up as individual energy storage investors,which are integrated with the stepwise carbon trading model to achieve and energy conversion is carried out by combining the stepped carbon trading model with the park.Secondly,the daily operation energy consumption of the comprehensive integrated energy park is taken as the upper objective function,and while the lower level is selected taken as the independent hydrogen and thermal energy storage planning cost as the objective function.In this way,to build a double-layer capacity optimization configuration model is established.The KKT constraints are used utilized to integrate the double-layer model and the large M method is employed for linearizedation,and then.Subsequently,the solver Gurobi is used to solve the problem.The calculation results show demonstrate that the energy storage as a separate sole investor to optimize the operation of the park can effectively reduce the carbon emissions released by during the system operation.In addition,it can economically reduce the construction costs of the park system while increasing energy storage revenues,thereby achieving the goals of both low-carbon and economic efficiency.
With the vigorous implementation of China's carbon peaking and carbon neutrality goals,renewable energy has gradually come into the public spotlight.The emergence of microgrids is conducive to the rapid absorption of renewable energy,making microgrid dispatching and optimization a research focal point.Traditional methods for low-carbon economic dispatching of microgrids encounter various challenges such as high data dimensionality,complex modeling,limited flexibility,and extended operation times.To address these challenges,a low-carbon economic optimization dispatching strategy for microgrids is proposed based on an improved twin delayed deep deterministic(TD3)policy gradient algorithm.Firstly,a statistical analysis on the strategy results of model predictive control(MPC)is conducted.The obtained operation rules of energy storage batteries are utilized to refine the self-learning rules of the TD3 algorithm,thereby effectively reducing the model's exploration area.Secondly,the dual buffer technique is introduced to address the issue of sample imbalance,achieving a dual enhancement in efficiency and robustness.Finally,the effectiveness of the proposed algorithm model is verified through the analysis of a case study of a demonstration microgrid.
Aiming at the issue of insufficient local adaptability in kernel density estimation algorithm for wind power interval prediction,we propose an adaptive kernel density estimation algorithm for wind power ultra-short-term interval prediction.Firstly,a long short term-memory wind power ultra-short-term prediction model is constructed based on the improved sequence to sequence(Seq2Seq)architecture with a dual-stage attention mechanism for wind power deterministic prediction.Subsequently,an proposed adaptive kernel density estimation algorithm is utilized to estimate the kernel density of the historical wind power prediction error.The proposed algorithm is capable of adaptively adjusting the bandwidth according to the kernel density estimation of the prediction error.Finally,the cumulative distribution function of the prediction error is obtained by integrating the kernel density estimation results.In addition,the upper and lower quantiles under different confidence coefficients are calculated,and the ultra-short-term prediction intervals of wind power under the corresponding confidence coefficients are obtained by adding the quantiles and the deterministic prediction values.Simulation analysis based on the data from a wind farm in Northwest China demonstrate that the proposed adaptive kernel density estimation algorithm exhibits superior adaptive ability to wind power prediction error data.Moreover,the effectiveness of this wind power interval prediction model is validated through these experiments.
